code: def gbm_check_variable_importance_and_model(data, target):
      fr = h2o.import_file(pyunit_utils.locate(data))
      fr[target] = fr[target].asfactor()
      variable_importance_same = []
      ntrees = 50
      for i in range(5):
          model_1 = H2OGradientBoostingEstimator(
              ntrees=ntrees, sample_rate=1, seed=1234
          )
          model_1.train(y=target, training_frame=fr)
          pred_1 = model_1.predict(fr)
          model_2 = H2OGradientBoostingEstimator(
              ntrees=ntrees, sample_rate=1, seed=1234
          )
          model_2.train(y=target, training_frame=fr)
          pred_2 = model_2.predict(fr)
  
          relative_importance1 = model_1.varimp()
          relative_importance2 = model_2.varimp()
          # This is a bug, will be fixed in PUBDEV-9000
          variable_importance_same.append(
              relative_importance1 == relative_importance2
          )
          print(relative_importance1)
          print(relative_importance2)
          # Even thought there is a difference in variable importance the models should be the same
          models_are_equal(model_1, model_2, pred_1, pred_2)
      return variable_importance_same
  
file_path: h2o-3/h2o-py/tests/testdir_algos/gbm/pyunit_gbm_reproducibility_variable_importance_different_but_same_model_large.py
imports: from builtins import range
  import sys
  import h2o
  from tests import pyunit_utils, assert_equals, compare_frames, assert_not_equal
  from h2o.estimators import H2OGradientBoostingEstimator
  from h2o.tree import H2OTree
label_desc: Use image node_id 3 for calling a global function with example usage: gbm_check_variable_importance_and_model(data, target) and returns: variable_importance_same